學術產出-Theses
Article View/Open
Publication Export
-
題名 以溝通模型模擬具有社會行為的虛擬人群
Simulating social behaviors of virtual crowd with a communication model作者 趙偉銘
Chao, Wei Ming貢獻者 李蔡彥
Li, Tsai Yen
趙偉銘
Chao, Wei Ming關鍵詞 人群模擬
群體動畫
溝通模型
情緒傳染
代理人模型
從眾效應
Crowd Simulation
Crowd Animation
Communication Model
Emotion Contagion
Agent-based Model
Bandwagon Effect日期 2010 上傳時間 9-May-2016 15:29:08 (UTC+8) 摘要 無論在電腦動畫、電玩或電影產業，利用電腦自動產生虛擬人群已逐漸成為不可或缺的要素之一。這些虛擬人群，往往是系統先賦與每個虛擬代理人(agent)基礎智能，然後藉由個體之間的互動法則所自動產生。然而，過去因為普遍未考量真實群體情境中的傳播與互動模式，使得虛擬人群所表現的群體行為與現實情況仍有些差距。因此，我們引用社會心理學文獻，建立一個具有溝通機制的人群模擬平台(IMCrowd)，以期自動產生與現實群眾動態更相似的模擬人群。IMCrowd是多代理人(Multi-agent)基礎的系統，其中每個虛擬代理人都具有區域的感知範圍與自主能力，因此他們能夠自動地與環境中的其它物件互動與反應。由於我們為IMCrowd所建立的溝通模型考量了社會心理學的理論，因此虛擬人群能浮現真實群體動態中的社會互動模式，如情緒傳染與從眾效應。本研究以IMCrowd執行了多種情境下群眾暴動與群眾控制的模擬，藉此展現本系統的應用將不僅可提升群體模擬的真實度，亦可做為社會心理學家研究群體行為的工具。
Using computer to automatically generate simulated crowd has become a trend in animation, computer game, and film productions. Many of these works were produced by modeling the intelligence of the agents in a crowd and their interactions with other nearby agents and the environment. However, the perceived facts or elicited emotions usually do not propagate in the crowd as they should in the real life. In this work we attempt to build up a communication model to simulate a large variety of crowd behaviors including the course of crowd formation. The proposed crowd simulation system, IMCrowd, has been implemented with a multi-agent system in which each agent has a local perception and autonomous abilities to improvise their actions. The algorithms used in our communication model in IMCrowd are based heavily on sociology research. Therefore, the collective behaviors will emerge out of the social process such as emotion contagion and conformity effect among individual agents. Several elaborate riot simulations and riot control simulations are demonstrated and reported in this thesis as the application examples of IMCrowd. Thus, we claim that IMCrowd may not only benefit on enhancing realism of crowd animation but also be useful in studying crowd behaviors such as panic, gathering, and riots.參考文獻 [1] Anderson, C., Keltner, D., and John, O.P. 2003. “Emotional Convergence Between People over Time,” Journal of Personality and Social Psychology, Vol. 84, No.5, pp. 1054-68. [2] Asch, S.E. 1955 “Opinions and social pressures,” Scientific American, Vol. 193 No. 5, pp. 31-35. [3] Aveni, A.F. 1977. “The Not-so-Lonely Crowd: Friendship Groups in Collective Behavior,” Sociometry, Vol. 40, No. 1, pp. 96-99. [4] Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., and Zdravkovic, V. 2008. “Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study,” in Proc. National Academy of Sciences, Vol.105, pp. 1232-1237. [5] Blumer, H. 1969. Symbolic Interactionism: Perspective and Method. Prentice Hall. [6] Boids, http://www.red3d.com/cwr/boids/ [7] Boss L.P. 1997. “Epidemic hysteria: a review of the published literature,” Epidemiologic Reviews. Vol.19, No. 2, pp. 233-243. [8] Chiang, Y.S. 2007. “Birds of Moderately Different Feathers: Bandwagon Dynamics and the Threshold Heterogeneity of Network Neighbors,” Journal of Mathematical Sociology, Vol. 31, No. 1, pp. 47–69. [9] Chwe, M.S-Y. 2000. “Communication and Coordination in Social Networks,’’ Review of Economic Studies, Vol. 67, No. 1, pp. 1–16. [10] Collins, R. 2008. Violence: A Micro-Sociological Theory. Princeton University Press. [11] Epstein, J.M. and Axtell, R. 1996. Growing Artificial Societies: social science from the bottom up. MIT Press. [12] EXODUS, the evacuation model for the safety industry, http://fseg.gre.ac.uk/exodus/ [13] Funge, J., Tu, X., and Terzopoulos, D. 1999. “Cognitive modeling: Knowledge, reasoning and planning for intelligent characters,” in Proc. of SIGGRAPH 99, pp. 29–38. [14] Gaad, C., Minderaa, R.B., and Keysers, C. 2007. “Facial expression: What the mirror neuron system can and cannot tell us,” Social Neuroscience, Vol. 2, No. 3-4, pp. 179-222. [15] Gilbert, N. and Terna, P. 2000. “How to build and use agent-based models in social science,” Mind & Society, Issue 1, Vol. 1, pp. 57-72. [16] Gilbert, N. and Troitzsch, K.G. 2005. Simulation for the Social Scientist. Open University Press, London, UK. [17] Gladwell, M. 2000. The Tipping Point: How Little Things Can Make a Big Difference, London: Little, Brown and Company. [18] Gnuplot, http://www.gnuplot.info/ [19] Goldstone, R.L. and Janssen, M.A. 2005. “Computational models of collective behavior,” Trends in Cognitive Science, Vol. 375. [20] Goleman, D. 2006. Social intelligent: The new science of human relationships, Bantam Dell Pub Group. [21] Granovetter, M. 1978. “Threshold Models of Collective Behavior,” The American Journal of Sociology, Vol. 83, No. 6.l, pp. 1420-1443. [22] Hamagami, T. and Hirata, H. 2003. “Method of crowd simulation by using multiagent on cellular automata,” in Proc. of IEEE/WIC International Conference on Intelligent Agent Technology (IAT’03). [23] Hatfield, E., Cacioppo, J.T., and Rapson, R.L. 1994. Emotional Contagion, Cambridge University Press. [24] Helbing, D., Farkas, I., and Vicsek, T. 2000. “Simulating dynamical features of escape panic,” Nature, Vol. 407, pp. 487-490. [25] Helbing, D., Molna¨r, P., Farkas I.J., and Bolay K. 2001. “Self-organizing pedestrian movement,” Environment and Planning B: Planning and Design, Vol. 28, pp. 361-383. [26] InfoNode, http://www.infonode.net/ [27] Jager, W., Popping, R., and van de Sande, H. 2001. “Clustering and Fighting in Two-party Crowds: Simulating the Approach-avoidance Conflict,” Journal of Artificial Societies and Social Simulation, Vol. 4, No. 3. [28] Java 6, http://java.sun.com/javase/6/ [29] Kauffman S. 1995. At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. Oxford University Press. [30] LeBon, G. 1895. The Crowd：A Study of the Popular Mind. [31] Lewin, K. 1935. A Dynamic Theory of Personality, New York: McGraw Hill Custom Publishing. [32] Li, T.Y., Liao, M.Y., and Tao, P.C. 2005. “IMNET: An Experimental Testbed for Extensible Multi-user Virtual Environment Systems,” in Proc. of International Conference on Computational Science and its Applications, LNCS 3480, O. Gervasi et al. (Eds.), Springer-Verlag Berlin Heidelberg, pp.957-966. [33] Luke S., Cioffi-Revilla C., Panait L., Sullivan K., and Balan G. 2005. “MASON: A Multi-Agent Simulation Environment,” Simulation, Vol. 81, No. 7, pp. 517-527. [34] Macy, M.W. and Willer, R. 2002. “From Factors to Actors: Computational Sociology and Agent-Based Modeling,” Annu. Rev. Sociol. Vol. 28, pp.143–166. [35] Marx, G.T., McAdam D. 1994. Collective Behavior and Social Movements: process and structure. Prentic hall. [36] MASON, Multi-Agent Simulator of Neighborhoods, http://www.cs.gmu.edu/~eclab/projects/mason/ [37] McPhail, C. 1994. “THE DARK SIDE OF PURPOSE: Individual and Collective Violence in Riots,” The Sociological Quarterly, Vol. 35, pp. 1-32. [38] McPhail, C. 1991. The Myth of the Madding Crowd, Aldine Transaction. [39] McPhail, C. 1983. “Individual and Collective Behaviors within Gatherings, Demonstrations, and Riots,” The Annual Review of Sociology, Vol. 9, pp. 579-600 [40] Miller, M.B. and Bassler, B.L. 2001. “Quorum sensing in bacteria,” Annu Rev Microbiol, Vol. 55, pp. 165-199. [41] Molnar, P. and Starke, J. 2001. “Control of distributed autonomous robotic systems using principles of pattern formation in nature and pedestrian behavior,” IEEE Trans. Syst. Man Cyb. B, Vol. 31, No. 3, pp. 433-436. [42] Musse, S.R. and Thalmann, D. 1997. “A Model of Human Crowd Behavior: Group Inter-Relationship and Collision Detection Analysis,” in Proc. of Eurographics Workshop, pp. 39-52. [43] Musse, S.R. and Thalmann, D. 2001. “Hierarchical Model for Real Time Simulation of Virtual Human Crowds,” IEEE Transactions on Visualization and Computer Graphics, Vol. 7, No. 2. [44] MyriadII, integrated crowd dynamics modeling suite, http://www.crowddynamics.com/Myriad%20II/Myriad%20II.htm [45] NetLogo, http://ccl.northwestern.edu/netlogo/ [46] Neumann, R., and Strack, F. 2000. “Mood Contagion: The Automatic Transfer of Mood Between Persons,” Journal of Personality and Social Psychology, Vol. 79, No. 2, pp. 211-223. [47] Ostrom, T. 1988. “Computer simulation: the third symbol system,” Journal of Experimental Social Psychology, Vol 24, pp. 381-392. [48] Pan, X., Han, C.S., and Law, K.H. 2005. “A Multi-agent Based Simulation Framework for the Study of Human and Social Behavior in Egress Analysis,” in Proc. of International Conference on Computing in Civil Engineering, Cancun, Mexico, July 12-15. [49] Pelechano, N., Allbeck, J.M., and Badler, N. 2007. “Controlling Individual Agents in High-Density Crowd Simulation,” in Proc. of the ACM SIGGRAPH/Eurographics symposium on Computer Animation. [50] Pelechano, N., AllBeck, J.M., and Badler, N. 2008. Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation), Morgan and Claypool Publishers. [51] Pelechano, N., O`Brien, K., Silverman, B., and Badler, N. 2005. “Crowd Simulation Incorporating Agent Psychological Models, Roles and Communication,” in the First International Workshop on Crowd Simulation. (V-CROWD`05). [52] Repast, http://repast.sourceforge.net/index.html [53] Reynolds, C.W. 1987. “Flocks, Herds and Schools: A distributed behavioral model,” in Proc. of the 14th annual conference on Computer graphics and interactive techniques, ACM Press, pp. 25–34. [54] Reynolds, C.W. 1999. “Steering behaviors for autonomous characters,” in Proc. of Game Developers Conference, pp. 763-782. [55] Rymill, S.J. and Dodgson, N.A. 2005. “A Psychologically-Based Simulation of Human Behavior,” Theory and Practice of Computer Graphics, pp. 35-42. [56] Schneirla, T.C. 1944. “A unique case of circular milling in ants, considered in relation to trail following and the general problem of orientation,” American Museum Novitates, No. 1253, pp. 1–26. [57] Shao, W. and Terzopoulos, D. 2005. “Autonomous Pedestrians,” Eurographics/ACM SIGGRAPH Symposium on Computer Animation. [58] Schelling, T. 1971. “Dynamic Model of Segregation,” Journal of Mathematical Sociology, Vol. 1, pp. 143-186. [59] Schelling, T. 1978. Micromotives and Macrobehaviors, New York: Norton & Company. [60] Shannon, C.E. 1948. “A Mathematical Theory of Communication”, Bell System Technical Journal, Vol. 27, pp. 379-423, 623-656. [61] Simulex, simulation of occupant evacuation, http://www.iesve.com/CONTENT/default.asp?page=s30_2 [62] Sommer, R. 1979. Personal Space. Englewood Cliffs, Prentice Hall. [63] STEPS, simulation of transient evacuation and pedestrian movements, http://www.mottmac.com/skillsandservices/software/stepssoftware/ [64] Still, G.K. 2000. Crowd Dynamics, PhD thesis, Warwick University, UK. [65] Sugarscape, http://sugarscape.sourceforge.net/sugarscape.html [66] Swam, http://www.swarm.org/ [67] Thalmann, D. and Musse, S.R. 2007. Crowd Simulation, Springer. [68] Treuille, A., Cooper, S., and Popović Z. 2006 “Continuum Crowds,” in Proc. ACM Transactions on Graphics (SIGGRAPH 2006), pp. 1160–1168. [69] Tucker, C.W., Schweingruber, D., and McPhail, C. 1999. “Simulating arcs and rings in gatherings,” International Journal of Human-Computer Systems, Vol 50, pp. 581-588. [70] Tu, X. and Terzopoulos, D. 1994. “Artificial Fishes: Physics, Locomotion, Perception, Behavior,” in Proc. of SIGGRAPH. [71] Villamil, M.B., Braun, A., and Musse, S.R. 2003. “A Rules-Based Model Used to Describe Group Dynamics for Games,” in Proc. of SIBGRAPI, IEEE, São Paulo, Brazil. 描述 碩士
國立政治大學
資訊科學學系
96753008資料來源 http://thesis.lib.nccu.edu.tw/record/#G0096753008 資料類型 thesis dc.contributor.advisor 李蔡彥 zh_TW dc.contributor.advisor Li, Tsai Yen en_US dc.contributor.author (Authors) 趙偉銘 zh_TW dc.contributor.author (Authors) Chao, Wei Ming en_US dc.creator (作者) 趙偉銘 zh_TW dc.creator (作者) Chao, Wei Ming en_US dc.date (日期) 2010 en_US dc.date.accessioned 9-May-2016 15:29:08 (UTC+8) - dc.date.available 9-May-2016 15:29:08 (UTC+8) - dc.date.issued (上傳時間) 9-May-2016 15:29:08 (UTC+8) - dc.identifier (Other Identifiers) G0096753008 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/95267 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 96753008 zh_TW dc.description.abstract (摘要) 無論在電腦動畫、電玩或電影產業，利用電腦自動產生虛擬人群已逐漸成為不可或缺的要素之一。這些虛擬人群，往往是系統先賦與每個虛擬代理人(agent)基礎智能，然後藉由個體之間的互動法則所自動產生。然而，過去因為普遍未考量真實群體情境中的傳播與互動模式，使得虛擬人群所表現的群體行為與現實情況仍有些差距。因此，我們引用社會心理學文獻，建立一個具有溝通機制的人群模擬平台(IMCrowd)，以期自動產生與現實群眾動態更相似的模擬人群。IMCrowd是多代理人(Multi-agent)基礎的系統，其中每個虛擬代理人都具有區域的感知範圍與自主能力，因此他們能夠自動地與環境中的其它物件互動與反應。由於我們為IMCrowd所建立的溝通模型考量了社會心理學的理論，因此虛擬人群能浮現真實群體動態中的社會互動模式，如情緒傳染與從眾效應。本研究以IMCrowd執行了多種情境下群眾暴動與群眾控制的模擬，藉此展現本系統的應用將不僅可提升群體模擬的真實度，亦可做為社會心理學家研究群體行為的工具。 zh_TW dc.description.abstract (摘要) Using computer to automatically generate simulated crowd has become a trend in animation, computer game, and film productions. Many of these works were produced by modeling the intelligence of the agents in a crowd and their interactions with other nearby agents and the environment. However, the perceived facts or elicited emotions usually do not propagate in the crowd as they should in the real life. In this work we attempt to build up a communication model to simulate a large variety of crowd behaviors including the course of crowd formation. The proposed crowd simulation system, IMCrowd, has been implemented with a multi-agent system in which each agent has a local perception and autonomous abilities to improvise their actions. The algorithms used in our communication model in IMCrowd are based heavily on sociology research. Therefore, the collective behaviors will emerge out of the social process such as emotion contagion and conformity effect among individual agents. Several elaborate riot simulations and riot control simulations are demonstrated and reported in this thesis as the application examples of IMCrowd. Thus, we claim that IMCrowd may not only benefit on enhancing realism of crowd animation but also be useful in studying crowd behaviors such as panic, gathering, and riots. en_US dc.description.tableofcontents CHAPTER 1 Introduction 1 1.1 Objectives 4 1.2 System Architecture Design 5 1.3 Thesis Overview 6 CHAPTER 2 Literature Review 7 2.1 Collective Behaviors in Social Psychology 8 2.2 Agent-based Models of Collective Behavior 10 2.3 Crowd Animation 17 CHAPTER 3 Agent Behavior Model 22 3.1 Physical Module 23 3.2 Perception Module 25 3.3 Primitive Behavior Module 26 3.3.1 Fundamental Behaviors 27 3.3.2 Group Motion 29 3.3.3 Obstacle Avoidance 35 3.3.4 Combining Steering Behaviors 40 CHAPTER 4 Communication Model 44 4.1 Communication Module 47 4.1.1 Design concepts 47 4.1.2 Communication Framework 48 4.1.3 Four States of a Normal Agent 50 4.1.4 Customization of Suggestive Message 53 4.1.5 Three Kinds of Signals 54 4.1.6 Summary of the Communication Process in IMCrowd 62 4.2 Action Selection Module 63 4.2.1 Leaving Action and Individual Action 64 4.2.2 Collective Action 64 CHAPTER 5 Experimental Results and Analysis 75 5.1 Experiment Environment 75 5.2 Evaluation of the Communication Model 78 5.2.1 Effect of the α Parameter 79 5.2.2 Effect of the β Parameter 80 5.2.3 Effect of the ρ Parameter 85 5.3 Effect of the γ Parameter and Interactive Obstacle 86 5.4 Experiments for Riot Simulation 90 5.4.1 The Size of Crowd 95 5.4.2 The Relative Size of the Parties 96 5.4.3 Initial Position Distribution of the Crowd 106 5.5 Riot Control Simulation 109 5.5.1 Three Policing Strategies 110 5.5.2 Experiment Design and Results of Riot Simulation with Polices 112 5.5.3 Superiority Strategy vs. Density Strategy in Case E and F 125 5.5.4 Superiority Strategy vs. Density Strategy in Case G and H 128 5.5.5 The Analysis of Entropy Strategy 134 5.5.6 Opposite Results of the Case G and Case H of Subsection 5.4.3 When Police Using Entropy or Superiority Strategy 137 CHAPTER 6 Conclustions and Future Work 138 6.1 Conclusion 138 6.2 Limiations and Future Work 139 REFERENCES 142 CHAPTER 1 Introduction 1 1.1 Objectives 4 1.2 System Architecture Design 5 1.3 Thesis Overview 6 CHAPTER 2 Literature Review 7 2.1 Collective Behaviors in Social Psychology 8 2.2 Agent-based Models of Collective Behavior 10 2.3 Crowd Animation 17 CHAPTER 3 Agent Behavior Model 22 3.1 Physical Module 23 3.2 Perception Module 25 3.3 Primitive Behavior Module 26 3.3.1 Fundamental Behaviors 27 3.3.2 Group Motion 29 3.3.3 Obstacle Avoidance 35 3.3.4 Combining Steering Behaviors 40 CHAPTER 4 Communication Model 44 4.1 Communication Module 47 4.1.1 Design concepts 47 4.1.2 Communication Framework 48 4.1.3 Four States of a Normal Agent 50 4.1.4 Customization of Suggestive Message 53 4.1.5 Three Kinds of Signals 54 4.1.6 Summary of the Communication Process in IMCrowd 62 4.2 Action Selection Module 63 4.2.1 Leaving Action and Individual Action 64 4.2.2 Collective Action 64 CHAPTER 5 Experimental Results and Analysis 75 5.1 Experiment Environment 75 5.2 Evaluation of the Communication Model 78 5.2.1 Effect of the α Parameter 79 5.2.2 Effect of the β Parameter 80 5.2.3 Effect of the ρ Parameter 85 5.3 Effect of the γ Parameter and Interactive Obstacle 86 5.4 Experiments for Riot Simulation 90 5.4.1 The Size of Crowd 95 5.4.2 The Relative Size of the Parties 96 5.4.3 Initial Position Distribution of the Crowd 106 5.5 Riot Control Simulation 109 5.5.1 Three Policing Strategies 110 5.5.2 Experiment Design and Results of Riot Simulation with Polices 112 5.5.3 Superiority Strategy vs. Density Strategy in Case E and F 125 5.5.4 Superiority Strategy vs. Density Strategy in Case G and H 128 5.5.5 The Analysis of Entropy Strategy 134 5.5.6 Opposite Results of the Case G and Case H of Subsection 5.4.3 When Police Using Entropy or Superiority Strategy 137 CHAPTER 6 Conclustions and Future Works 138 6.1 Conclusion 138 6.2 Future Works 139 REFERENCES 141 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0096753008 en_US dc.subject (關鍵詞) 人群模擬 zh_TW dc.subject (關鍵詞) 群體動畫 zh_TW dc.subject (關鍵詞) 溝通模型 zh_TW dc.subject (關鍵詞) 情緒傳染 zh_TW dc.subject (關鍵詞) 代理人模型 zh_TW dc.subject (關鍵詞) 從眾效應 zh_TW dc.subject (關鍵詞) Crowd Simulation en_US dc.subject (關鍵詞) Crowd Animation en_US dc.subject (關鍵詞) Communication Model en_US dc.subject (關鍵詞) Emotion Contagion en_US dc.subject (關鍵詞) Agent-based Model en_US dc.subject (關鍵詞) Bandwagon Effect en_US dc.title (題名) 以溝通模型模擬具有社會行為的虛擬人群 zh_TW dc.title (題名) Simulating social behaviors of virtual crowd with a communication model en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Anderson, C., Keltner, D., and John, O.P. 2003. “Emotional Convergence Between People over Time,” Journal of Personality and Social Psychology, Vol. 84, No.5, pp. 1054-68. [2] Asch, S.E. 1955 “Opinions and social pressures,” Scientific American, Vol. 193 No. 5, pp. 31-35. [3] Aveni, A.F. 1977. “The Not-so-Lonely Crowd: Friendship Groups in Collective Behavior,” Sociometry, Vol. 40, No. 1, pp. 96-99. [4] Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., and Zdravkovic, V. 2008. “Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study,” in Proc. National Academy of Sciences, Vol.105, pp. 1232-1237. [5] Blumer, H. 1969. Symbolic Interactionism: Perspective and Method. Prentice Hall. [6] Boids, http://www.red3d.com/cwr/boids/ [7] Boss L.P. 1997. “Epidemic hysteria: a review of the published literature,” Epidemiologic Reviews. Vol.19, No. 2, pp. 233-243. [8] Chiang, Y.S. 2007. “Birds of Moderately Different Feathers: Bandwagon Dynamics and the Threshold Heterogeneity of Network Neighbors,” Journal of Mathematical Sociology, Vol. 31, No. 1, pp. 47–69. [9] Chwe, M.S-Y. 2000. “Communication and Coordination in Social Networks,’’ Review of Economic Studies, Vol. 67, No. 1, pp. 1–16. [10] Collins, R. 2008. Violence: A Micro-Sociological Theory. Princeton University Press. [11] Epstein, J.M. and Axtell, R. 1996. Growing Artificial Societies: social science from the bottom up. MIT Press. [12] EXODUS, the evacuation model for the safety industry, http://fseg.gre.ac.uk/exodus/ [13] Funge, J., Tu, X., and Terzopoulos, D. 1999. “Cognitive modeling: Knowledge, reasoning and planning for intelligent characters,” in Proc. of SIGGRAPH 99, pp. 29–38. [14] Gaad, C., Minderaa, R.B., and Keysers, C. 2007. “Facial expression: What the mirror neuron system can and cannot tell us,” Social Neuroscience, Vol. 2, No. 3-4, pp. 179-222. [15] Gilbert, N. and Terna, P. 2000. “How to build and use agent-based models in social science,” Mind & Society, Issue 1, Vol. 1, pp. 57-72. [16] Gilbert, N. and Troitzsch, K.G. 2005. Simulation for the Social Scientist. Open University Press, London, UK. [17] Gladwell, M. 2000. The Tipping Point: How Little Things Can Make a Big Difference, London: Little, Brown and Company. [18] Gnuplot, http://www.gnuplot.info/ [19] Goldstone, R.L. and Janssen, M.A. 2005. “Computational models of collective behavior,” Trends in Cognitive Science, Vol. 375. [20] Goleman, D. 2006. Social intelligent: The new science of human relationships, Bantam Dell Pub Group. [21] Granovetter, M. 1978. “Threshold Models of Collective Behavior,” The American Journal of Sociology, Vol. 83, No. 6.l, pp. 1420-1443. [22] Hamagami, T. and Hirata, H. 2003. “Method of crowd simulation by using multiagent on cellular automata,” in Proc. of IEEE/WIC International Conference on Intelligent Agent Technology (IAT’03). [23] Hatfield, E., Cacioppo, J.T., and Rapson, R.L. 1994. Emotional Contagion, Cambridge University Press. [24] Helbing, D., Farkas, I., and Vicsek, T. 2000. “Simulating dynamical features of escape panic,” Nature, Vol. 407, pp. 487-490. [25] Helbing, D., Molna¨r, P., Farkas I.J., and Bolay K. 2001. “Self-organizing pedestrian movement,” Environment and Planning B: Planning and Design, Vol. 28, pp. 361-383. [26] InfoNode, http://www.infonode.net/ [27] Jager, W., Popping, R., and van de Sande, H. 2001. “Clustering and Fighting in Two-party Crowds: Simulating the Approach-avoidance Conflict,” Journal of Artificial Societies and Social Simulation, Vol. 4, No. 3. [28] Java 6, http://java.sun.com/javase/6/ [29] Kauffman S. 1995. At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. Oxford University Press. [30] LeBon, G. 1895. The Crowd：A Study of the Popular Mind. [31] Lewin, K. 1935. A Dynamic Theory of Personality, New York: McGraw Hill Custom Publishing. [32] Li, T.Y., Liao, M.Y., and Tao, P.C. 2005. “IMNET: An Experimental Testbed for Extensible Multi-user Virtual Environment Systems,” in Proc. of International Conference on Computational Science and its Applications, LNCS 3480, O. Gervasi et al. (Eds.), Springer-Verlag Berlin Heidelberg, pp.957-966. [33] Luke S., Cioffi-Revilla C., Panait L., Sullivan K., and Balan G. 2005. “MASON: A Multi-Agent Simulation Environment,” Simulation, Vol. 81, No. 7, pp. 517-527. [34] Macy, M.W. and Willer, R. 2002. “From Factors to Actors: Computational Sociology and Agent-Based Modeling,” Annu. Rev. Sociol. Vol. 28, pp.143–166. [35] Marx, G.T., McAdam D. 1994. Collective Behavior and Social Movements: process and structure. Prentic hall. [36] MASON, Multi-Agent Simulator of Neighborhoods, http://www.cs.gmu.edu/~eclab/projects/mason/ [37] McPhail, C. 1994. “THE DARK SIDE OF PURPOSE: Individual and Collective Violence in Riots,” The Sociological Quarterly, Vol. 35, pp. 1-32. [38] McPhail, C. 1991. The Myth of the Madding Crowd, Aldine Transaction. [39] McPhail, C. 1983. “Individual and Collective Behaviors within Gatherings, Demonstrations, and Riots,” The Annual Review of Sociology, Vol. 9, pp. 579-600 [40] Miller, M.B. and Bassler, B.L. 2001. “Quorum sensing in bacteria,” Annu Rev Microbiol, Vol. 55, pp. 165-199. [41] Molnar, P. and Starke, J. 2001. “Control of distributed autonomous robotic systems using principles of pattern formation in nature and pedestrian behavior,” IEEE Trans. Syst. Man Cyb. B, Vol. 31, No. 3, pp. 433-436. [42] Musse, S.R. and Thalmann, D. 1997. “A Model of Human Crowd Behavior: Group Inter-Relationship and Collision Detection Analysis,” in Proc. of Eurographics Workshop, pp. 39-52. [43] Musse, S.R. and Thalmann, D. 2001. “Hierarchical Model for Real Time Simulation of Virtual Human Crowds,” IEEE Transactions on Visualization and Computer Graphics, Vol. 7, No. 2. [44] MyriadII, integrated crowd dynamics modeling suite, http://www.crowddynamics.com/Myriad%20II/Myriad%20II.htm [45] NetLogo, http://ccl.northwestern.edu/netlogo/ [46] Neumann, R., and Strack, F. 2000. “Mood Contagion: The Automatic Transfer of Mood Between Persons,” Journal of Personality and Social Psychology, Vol. 79, No. 2, pp. 211-223. [47] Ostrom, T. 1988. “Computer simulation: the third symbol system,” Journal of Experimental Social Psychology, Vol 24, pp. 381-392. [48] Pan, X., Han, C.S., and Law, K.H. 2005. “A Multi-agent Based Simulation Framework for the Study of Human and Social Behavior in Egress Analysis,” in Proc. of International Conference on Computing in Civil Engineering, Cancun, Mexico, July 12-15. [49] Pelechano, N., Allbeck, J.M., and Badler, N. 2007. “Controlling Individual Agents in High-Density Crowd Simulation,” in Proc. of the ACM SIGGRAPH/Eurographics symposium on Computer Animation. [50] Pelechano, N., AllBeck, J.M., and Badler, N. 2008. Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation), Morgan and Claypool Publishers. [51] Pelechano, N., O`Brien, K., Silverman, B., and Badler, N. 2005. “Crowd Simulation Incorporating Agent Psychological Models, Roles and Communication,” in the First International Workshop on Crowd Simulation. (V-CROWD`05). [52] Repast, http://repast.sourceforge.net/index.html [53] Reynolds, C.W. 1987. “Flocks, Herds and Schools: A distributed behavioral model,” in Proc. of the 14th annual conference on Computer graphics and interactive techniques, ACM Press, pp. 25–34. [54] Reynolds, C.W. 1999. “Steering behaviors for autonomous characters,” in Proc. of Game Developers Conference, pp. 763-782. [55] Rymill, S.J. and Dodgson, N.A. 2005. “A Psychologically-Based Simulation of Human Behavior,” Theory and Practice of Computer Graphics, pp. 35-42. [56] Schneirla, T.C. 1944. “A unique case of circular milling in ants, considered in relation to trail following and the general problem of orientation,” American Museum Novitates, No. 1253, pp. 1–26. [57] Shao, W. and Terzopoulos, D. 2005. “Autonomous Pedestrians,” Eurographics/ACM SIGGRAPH Symposium on Computer Animation. [58] Schelling, T. 1971. “Dynamic Model of Segregation,” Journal of Mathematical Sociology, Vol. 1, pp. 143-186. [59] Schelling, T. 1978. Micromotives and Macrobehaviors, New York: Norton & Company. [60] Shannon, C.E. 1948. “A Mathematical Theory of Communication”, Bell System Technical Journal, Vol. 27, pp. 379-423, 623-656. [61] Simulex, simulation of occupant evacuation, http://www.iesve.com/CONTENT/default.asp?page=s30_2 [62] Sommer, R. 1979. Personal Space. Englewood Cliffs, Prentice Hall. [63] STEPS, simulation of transient evacuation and pedestrian movements, http://www.mottmac.com/skillsandservices/software/stepssoftware/ [64] Still, G.K. 2000. Crowd Dynamics, PhD thesis, Warwick University, UK. [65] Sugarscape, http://sugarscape.sourceforge.net/sugarscape.html [66] Swam, http://www.swarm.org/ [67] Thalmann, D. and Musse, S.R. 2007. Crowd Simulation, Springer. [68] Treuille, A., Cooper, S., and Popović Z. 2006 “Continuum Crowds,” in Proc. ACM Transactions on Graphics (SIGGRAPH 2006), pp. 1160–1168. [69] Tucker, C.W., Schweingruber, D., and McPhail, C. 1999. “Simulating arcs and rings in gatherings,” International Journal of Human-Computer Systems, Vol 50, pp. 581-588. [70] Tu, X. and Terzopoulos, D. 1994. “Artificial Fishes: Physics, Locomotion, Perception, Behavior,” in Proc. of SIGGRAPH. [71] Villamil, M.B., Braun, A., and Musse, S.R. 2003. “A Rules-Based Model Used to Describe Group Dynamics for Games,” in Proc. of SIBGRAPI, IEEE, São Paulo, Brazil. zh_TW
